Autonomous vehicles use various computing systems to aid in the transport of passengers from one location to another. Some autonomous vehicles may require some initial input or continuous input from an operator, such as a pilot, driver, or passenger. Other systems, for example autopilot systems, may be used only when the system has been engaged, which permits the operator to switch from a manual mode (where the operator exercises a high degree of control over the movement of the vehicle) to an autonomous mode (where the vehicle essentially drives itself) to modes that lie somewhere in between.
These vehicles are equipped with various types of sensors in order to detect objects in the surroundings. For example, autonomous vehicles may include lasers, sonar, radar, cameras, and other devices which scan and record data from the vehicle's surroundings. These devices in combination (and in some cases alone) may be used to build 3D models of the objects detected in the vehicle's surrounding. However, once an object is detected by an autonomous vehicle, identifying what the object actually is (car, building, person, other vehicle, etc.) can be very difficult.
For example, pedestrians may be difficult to differentiate from small vehicles or bicyclists. In order to identify these objects in real time, the vehicle's computer is trained, for example by collecting and providing numerous images of pedestrians and using machine learning techniques, in order to enable the computer to identify similar shapes as pedestrians. This may require a significant amount of resources, both to collect the images and to process the data.